import cv2
import dlib
import numpy as np
import PIL.Image
import scipy.ndimage


class Normal_Image():
    def __init__(self,
                 predictor_file='data/shape_predictor_68_face_landmarks.dat'):
        self.detector = dlib.get_frontal_face_detector()
        self.predictor = dlib.shape_predictor(predictor_file)

    def run(self, image):
        gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        dets = self.detector(gray_img, 1)
        for detection in dets:
            face_landmarks = [
                (item.x, item.y)
                for item in self.predictor(image, detection).parts()
            ]
            return self.image_align(cv2.cvtColor(image, cv2.COLOR_BGR2RGB),
                                    face_landmarks)

    def image_align(self,
                    src_file,
                    face_landmarks,
                    output_size=1024,
                    transform_size=4096,
                    enable_padding=True,
                    x_scale=1,
                    y_scale=1,
                    em_scale=0.1,
                    alpha=False,
                    raw_data=True):
        # Align function from FFHQ dataset pre-processing step
        # https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py

        lm = np.array(face_landmarks)
        lm_chin = lm[0:17]  # left-right # noqa
        lm_eyebrow_left = lm[17:22]  # left-right # noqa
        lm_eyebrow_right = lm[22:27]  # left-right # noqa
        lm_nose = lm[27:31]  # top-down # noqa
        lm_nostrils = lm[31:36]  # top-down # noqa
        lm_eye_left = lm[36:42]  # left-clockwise
        lm_eye_right = lm[42:48]  # left-clockwise
        lm_mouth_outer = lm[48:60]  # left-clockwise
        lm_mouth_inner = lm[60:68]  # left-clockwise # noqa

        # Calculate auxiliary vectors.
        eye_left = np.mean(lm_eye_left, axis=0)
        eye_right = np.mean(lm_eye_right, axis=0)
        eye_avg = (eye_left + eye_right) * 0.5
        eye_to_eye = eye_right - eye_left
        mouth_left = lm_mouth_outer[0]
        mouth_right = lm_mouth_outer[6]
        mouth_avg = (mouth_left + mouth_right) * 0.5
        eye_to_mouth = mouth_avg - eye_avg

        # Choose oriented crop rectangle.
        x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
        x /= np.hypot(*x)
        x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
        x *= x_scale
        y = np.flipud(x) * [-y_scale, y_scale]
        c = eye_avg + eye_to_mouth * em_scale
        quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
        qsize = np.hypot(*x) * 2

        # Load in-the-wild image.
        # if not os.path.isfile(src_file):
        #     print('\nCannot find source image. Please run "--wilds" before "--align".') # isort:skip  # noqa
        #     return
        if (raw_data):
            img = PIL.Image.fromarray(src_file).convert('RGB')
        else:
            img = PIL.Image.open(src_file).convert('RGBA').convert('RGB')

        # Shrink.
        shrink = int(np.floor(qsize / output_size * 0.5))
        if shrink > 1:
            rsize = (int(np.rint(float(img.size[0]) / shrink)),
                     int(np.rint(float(img.size[1]) / shrink)))
            img = img.resize(rsize, PIL.Image.ANTIALIAS)
            quad /= shrink
            qsize /= shrink

        # Crop.
        border = max(int(np.rint(qsize * 0.1)), 3)
        crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))),
                int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
        crop = (max(crop[0] - border, 0), max(crop[1] - border, 0),
                min(crop[2] + border,
                    img.size[0]), min(crop[3] + border, img.size[1]))
        if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
            img = img.crop(crop)
            quad -= crop[0:2]
        if (img.size[0] < 128):
            return None
        # Pad.
        pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))),
               int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
        pad = (max(-pad[0] + border,
                   0), max(-pad[1] + border,
                           0), max(pad[2] - img.size[0] + border,
                                   0), max(pad[3] - img.size[1] + border, 0))
        if enable_padding and max(pad) > border - 4:
            pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
            img = np.pad(np.float32(img),
                         ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)),
                         'reflect')
            h, w, _ = img.shape
            y, x, _ = np.ogrid[:h, :w, :1]
            mask = np.maximum(
                1.0 - np.minimum(
                    np.float32(x) / pad[0],
                    np.float32(w - 1 - x) / pad[2]), 1.0 - np.minimum(
                        np.float32(y) / pad[1],
                        np.float32(h - 1 - y) / pad[3]))
            blur = qsize * 0.02
            img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) -
                    img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
            img += (np.median(img, axis=(0, 1)) - img) * np.clip(
                mask, 0.0, 1.0)
            img = np.uint8(np.clip(np.rint(img), 0, 255))
            if alpha:
                mask = 1 - np.clip(3.0 * mask, 0.0, 1.0)
                mask = np.uint8(np.clip(np.rint(mask * 255), 0, 255))
                img = np.concatenate((img, mask), axis=2)
                img = PIL.Image.fromarray(img, 'RGBA')
            else:
                img = PIL.Image.fromarray(img, 'RGB')
            quad += pad[:2]

        # Transform.
        img = img.transform((transform_size, transform_size), PIL.Image.QUAD,
                            (quad + 0.5).flatten(), PIL.Image.BILINEAR)
        if output_size < transform_size:
            img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
        return img
